Pinecone

The Pinecone Data Connector allows users to store and query text embeddings as vectors. Users can then perform similarity search operations on stored vectors.

#Release stage

Alpha

#Configuration

There are two things needed to configure a Pinecone connector: Pinecone URL (corresponding to your index) and an API key. Firstly, users need to create an index on https://www.pinecone.io. Once an index is created, the URL and API key can be used to configure a connector as mentioned below - For the up-to-date configuration, refer to here.

FieldTypeNote
url*stringFill your Pinecone url here
api_key*stringFill your Pinecone api_key here

#No-code Setup

To create a Pinecone data connector effortlessly, follow these steps:

  1. Go to the Resource page and click Add Resource
  2. Select Pinecone
  3. Fill in the required fields
  4. Optionally, give a short description in the Description field

#Low-code Setup

For advanced users who prefer a more hands-on approach, you can create a Pinecone data connector using the following cURL commands:

  1. Create a Pinecone data connector:
cURL
Copy

curl --location http://localhost:8080/vdp/v1alpha/users/<user-id>/connectors
--header 'Authorization: Bearer <Your-API-Token>' \
--data '{
"id": "pinecone",
"connector_definition_name": "connector-definitions/data-pinecone",
"description": "The Pinecone vector database",
"configuration": {
"url": <Pinecone host address>,
"api_key": <api key>
}
}'

For other operations, please refer to the VDP Protobufs.

#Supported Tasks

The Pinecone connector supports two tasks: Upsert and Query.

  1. Upsert - use this to insert a new vector embedding or modify existing ones. It accepts an array of vectors values as a required parameter. Where each vector is an object with an id and an array of floating point numbers.

    Ref: https://docs.pinecone.io/reference/upsert


{
"id": 1,
"values": [1.35, -1.3, 1.356, 0.356, ...]
}

  1. Query - use this to perform a similarity search on stored vectors. It requires 2 parameters: top_k - the number of matches to return, and vector - the query vector which is an array of floating point numbers.

    Ref: https://docs.pinecone.io/reference/query


{
"top_k": 5,
"vector": [1.35, -1.3, 1.356, 0.356, ...]
}

Last updated: 1/8/2024, 8:56:57 AM